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dc.contributor.author | Bentahar, Sara | |
dc.contributor.author | Gómez-Gaviro, María Victoria | |
dc.contributor.author | Desco, Manuel | |
dc.contributor.author | Ripoll, Jorge | |
dc.contributor.author | Fernández, Roberto | |
dc.date.accessioned | 2024-07-04T12:16:35Z | |
dc.date.available | 2024-07-04T12:16:35Z | |
dc.date.issued | 2024-05-27 | |
dc.identifier.citation | Sci Rep. 2024 May 27;14(1):12084. | es_ES |
dc.identifier.uri | http://hdl.handle.net/20.500.12105/20076 | |
dc.description.abstract | Selective Plane Illumination Microscopy (SPIM) has become an emerging technology since its first application for 3D in-vivo imaging of the development of a living organism. An extensive number of works have been published, improving both the speed of acquisition and the resolution of the systems. Furthermore, multispectral imaging allows the effective separation of overlapping signals associated with different fluorophores from the spectrum over the whole field-of-view of the analyzed sample. To eliminate the need of using fluorescent dyes, this technique can also be applied to autofluorescence imaging. However, the effective separation of the overlapped spectra in autofluorescence imaging necessitates the use of mathematical tools. In this work, we explore the application of a method based on Principal Component Analysis (PCA) that enables tissue characterization upon spectral autofluorescence data without the use of fluorophores. Thus, enabling the separation of different tissue types in fixed and living samples with no need of staining techniques. Two procedures are described for acquiring spectral data, including a single excitation based method and a multi-excitation scanning approach. In both cases, we demonstrate the effective separation of various tissue types based on their unique autofluorescence spectra. | es_ES |
dc.description.sponsorship | RF acknowledges funding from Ministerio de Ciencia e Innovación of Spain (project PID2021-123124OB-I00). JR acknowledges funding from the Ministerio de Ciencia e Innovación (PID2020-115088RB-I00, “BEHAVE3D”). MD and MVG-G acknowledge funding from Instituto de Salud Carlos III through the project DTS22 /00030. Tis project was co-funded by the European Union (ERDF, “A way to make Europe”), partially supported by Comunidad de Madrid (S2017/BMD-3867 RENIM-CM), and co-fnanced by European Structural and Investment Fund. Te CNIC is supported by Instituto de Salud Carlos III (ISCIII), Ministerio de Ciencia e Innovación (MCIN) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Nature Publishing Group | es_ES |
dc.type.hasVersion | VoR | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject.mesh | Principal Component Analysis | es_ES |
dc.subject.mesh | Optical Imaging | es_ES |
dc.subject.mesh | Animals | es_ES |
dc.subject.mesh | Microscopy, Fluorescence | es_ES |
dc.subject.mesh | Mice | es_ES |
dc.subject.mesh | Fluorescent Dyes | es_ES |
dc.subject.mesh | Imaging, Three-Dimensional | es_ES |
dc.title | Multispectral imaging for characterizing autofluorescent tissues. | es_ES |
dc.type | journal article | es_ES |
dc.rights.license | Atribución 4.0 Internacional | * |
dc.identifier.pubmedID | 38802477 | es_ES |
dc.format.volume | 14 | es_ES |
dc.format.number | 1 | es_ES |
dc.format.page | 12084 | es_ES |
dc.identifier.doi | 10.1038/s41598-024-61020-7 | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |
dc.contributor.funder | Instituto de Salud Carlos III | es_ES |
dc.contributor.funder | Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF) | es_ES |
dc.contributor.funder | Comunidad de Madrid (España) | es_ES |
dc.contributor.funder | Fundación ProCNIC | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación. Centro de Excelencia Severo Ochoa (España) | es_ES |
dc.description.peerreviewed | Sí | es_ES |
dc.identifier.e-issn | 2045-2322 | es_ES |
dc.relation.publisherversion | 10.1038/s41598-024-61020-7 | es_ES |
dc.identifier.journal | Scientific reports | es_ES |
dc.repisalud.orgCNIC | CNIC::Unidades técnicas::Imagen Avanzada | es_ES |
dc.repisalud.institucion | CNIC | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/PID2021-123124OB-I00 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/PID2020-115088RB-I00 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/DTS22/00030 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/S2017/BMD-3867 | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/ES/SEV-2015-0505 | es_ES |